The Community for Technology Leaders
Green Image
Issue No. 01 - Jan. (2016 vol. 65)
ISSN: 0018-9340
pp: 187-202
Yanchao Lu , Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Donghong Wu , Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Bingsheng He , School of Computer Engineering, Nanyang Technological University, Nanyang Ave, Singapore
Xueyan Tang , School of Computer Engineering, Nanyang Technological University, Nanyang Ave, Singapore
Jianliang Xu , Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Minyi Guo , Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
ABSTRACT
Modern DRAM architectures allow a number of low-power states on individual memory ranks for advanced power management. Many previous studies have taken advantage of demotions on low-power states for energy saving. However, most of the demotion schemes are statically performed on a limited number of pre-selected low-power states, and are suboptimal for different workloads and memory architectures. Even worse, the idle periods are often too short for effective power state transitions, especially for memory intensive applications. Wrong decisions on power state transition incur significant energy and delay penalties. In this paper, we propose a novel memory system design named RAMZzz with rank-aware energy saving optimizations including dynamic page migrations and adaptive demotions. Specifically, we group the pages with similar access locality into the same rank with dynamic page migrations. Ranks have their hotness: hot ranks are kept busy for high utilization and cold ranks can have more lengthy idle periods for power state transitions. We further develop adaptive state demotions by considering all low-power states for each rank and a prediction model to estimate the power-down timeout among states. We experimentally compare our algorithm with other energy saving policies with cycle-accurate simulation. Experiments with benchmark workloads show that RAMZzz achieves significant improvement on energy-delay $_$^2$_$ and energy consumption over other energy saving techniques.
INDEX TERMS
Random access memory, Memory architecture, Predictive models, Memory management, Power demand, Adaptation models, Energy consumption
CITATION

Y. Lu, D. Wu, B. He, X. Tang, J. Xu and M. Guo, "Rank-Aware Dynamic Migrations and Adaptive Demotions for DRAM Power Management," in IEEE Transactions on Computers, vol. 65, no. 1, pp. 187-202, 2016.
doi:10.1109/TC.2015.2409847
220 ms
(Ver 3.3 (11022016))